Multiple criteria decision making: the case for interdependence
Computers and Operations Research
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
On the hardness of offline multi-objective optimization
Evolutionary Computation
Objective reduction using a feature selection technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Online Objective Reduction to Deal with Many-Objective Problems
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Quantifying the effects of objective space dimension in evolutionary multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Objective space partitioning using conflict information for many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
International Journal of Hybrid Intelligent Systems
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In a previous work we proposed a scheme for partitioning the objective space using the conflict information of the current Pareto front approximation found by an underlying multi-objective evolutionary algorithm. Since that scheme introduced additional parameters that have to be set by the user, in this paper we propose important modifications in order to automatically set those parameters. Such parameters control the number of solutions devoted to explore each objective subspace, and the number of generations to create a new partition. Our experimental results show that the new adaptive scheme performs as good as the nonadaptive scheme, and in some cases it outperforms the original scheme.